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The econometric analysis of retail rents in Singapore

The econometric analysis of retail rents in Singapore PurposePrimarily based on Alonso’s bid-rent model, the purpose of this paper is to examine the dynamics of the Singapore’s overall retail rental market by adopting a vector error correction model (VECM) estimation.Design/methodology/approachThis paper uses the proxy for the overall retail rental value, which is indicated by a combination of the shop rent index from 2004 to 2013 and the retail rent index (RRI) in 2014, maintained by the Urban Redevelopment Authority (URA). The independent factors are the real gross domestic product (GDP), monthly earnings of individuals and vacancy rates (VR).FindingsSuch a behavioral model examines the dynamic structures that overshoot and/or diverge from equilibrium.Research limitations/implicationsThe variables LOGGDP and VR are co-integrated of order one, I(1), while variables LOGME and LOGSRI are co-integrated of order two, I(2), to enable them to be employed in the VECM model.Practical implicationsThe VECM model shows a good fit that allows the error correction term (ecm) together with the economic, financial and rental variables to jointly explain about 79.2 percent of the variation in the overall RRI. With a positive CoinEq1 coefficient that is positive and statistically significant at 5 percent level, it would take a long time for the system to return to its equilibrium once it has been shocked. Another variable that shows significant explanatory relationships includes past rents (index points) in the second order lags [D(LOGSRI(−2))]. The variable [D(LOGGDP(−3))], with a significant t-statistic value at 2.916, also helps to explain the changes in the overall rents.Social implicationsThis paper highlights the importance of the first and third differences of the lagged macroeconomic variables of the monthly earnings of individuals is moderately significant. The VR in the first and second differences is significant in accounting for the variation in changes of overall retail rents with their t-statistics values being above 3.0. It is thus meaningful for policy makers to so enhance their in-depth understanding.Originality/valueThis paper fulfills an identified need to study how the results from the ex post forecasting estimates from the VECM for overall retail rents in Singapore can be enabled. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Property Investment & Finance Emerald Publishing

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1463-578X
DOI
10.1108/JPIF-05-2016-0031
Publisher site
See Article on Publisher Site

Abstract

PurposePrimarily based on Alonso’s bid-rent model, the purpose of this paper is to examine the dynamics of the Singapore’s overall retail rental market by adopting a vector error correction model (VECM) estimation.Design/methodology/approachThis paper uses the proxy for the overall retail rental value, which is indicated by a combination of the shop rent index from 2004 to 2013 and the retail rent index (RRI) in 2014, maintained by the Urban Redevelopment Authority (URA). The independent factors are the real gross domestic product (GDP), monthly earnings of individuals and vacancy rates (VR).FindingsSuch a behavioral model examines the dynamic structures that overshoot and/or diverge from equilibrium.Research limitations/implicationsThe variables LOGGDP and VR are co-integrated of order one, I(1), while variables LOGME and LOGSRI are co-integrated of order two, I(2), to enable them to be employed in the VECM model.Practical implicationsThe VECM model shows a good fit that allows the error correction term (ecm) together with the economic, financial and rental variables to jointly explain about 79.2 percent of the variation in the overall RRI. With a positive CoinEq1 coefficient that is positive and statistically significant at 5 percent level, it would take a long time for the system to return to its equilibrium once it has been shocked. Another variable that shows significant explanatory relationships includes past rents (index points) in the second order lags [D(LOGSRI(−2))]. The variable [D(LOGGDP(−3))], with a significant t-statistic value at 2.916, also helps to explain the changes in the overall rents.Social implicationsThis paper highlights the importance of the first and third differences of the lagged macroeconomic variables of the monthly earnings of individuals is moderately significant. The VR in the first and second differences is significant in accounting for the variation in changes of overall retail rents with their t-statistics values being above 3.0. It is thus meaningful for policy makers to so enhance their in-depth understanding.Originality/valueThis paper fulfills an identified need to study how the results from the ex post forecasting estimates from the VECM for overall retail rents in Singapore can be enabled.

Journal

Journal of Property Investment & FinanceEmerald Publishing

Published: Sep 5, 2016

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